University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.
INSERM U1194 IRCM, Montpellier, France.
Br J Radiol. 2021 Sep 1;94(1125):20210032. doi: 10.1259/bjr.20210032. Epub 2021 May 7.
Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.
放射组学是提取大量定量成像特征,旨在检测与有用的临床结果相关的信息。特征是在划定感兴趣区域后,从单一或组合的成像方式(包括 X 射线、超声、CT、PET/CT 和 MRI)中提取出来的。由于具有很高的维度,因此分析过程需要使用人工智能算法。放射组学最初是为放射诊断性能开发的,现在已经被转化为放射肿瘤学,主要用于预测肿瘤反应和患者预后,但也开发了其他应用,如剂量描绘、副作用预测和质量保证。在妇科癌症中,大多数研究都集中在放化疗后宫颈癌的结果上。本综述强调了这一新工具对放射肿瘤学家的作用,特别关注女性泌尿生殖系统肿瘤学。